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Solving Ill-posed Problems Using Data Assimilation. Application to optical flow estimation

Abstract : Data Assimilation is a mathematical framework used in environmental sciences to improve forecasts performed by meteorological, oceanographic or air quality simulation models. Data Assimilation techniques require the resolution of a system with three components: one describing the temporal evolution of a state vector, one coupling the observations and the state vector, and one defining the initial condition. In this article, we use this framework to study a class of ill-posed Image Processing problems, usually solved by spatial and temporal regularization techniques. A generic approach is defined to convert an ill-posed Image Processing problem in terms of a Data Assimilation system. This method is illustrated on the determination of optical flow from a sequence of images. The resulting software has two advantages: a quality criterion on input data is used for weighting their contribution in the computation of the solution and a dynamic model is proposed to ensure a significant temporal regularity on the solution.
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Submitted on : Wednesday, January 8, 2014 - 10:50:16 AM
Last modification on : Friday, January 21, 2022 - 3:21:57 AM
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  • HAL Id : inria-00567444, version 1


Dominique Béréziat, Isabelle Herlin. Solving Ill-posed Problems Using Data Assimilation. Application to optical flow estimation. VISAPP - International Conference on Computer Vision Theory and Applications, Feb 2009, Lisboa, Portugal. pp.594-602. ⟨inria-00567444⟩



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